import VectorIndexApi, { CreateIndexOptions } from './VectorIndexApi'
import VectorDataApi from './VectorDataApi'
import { Fetch } from './fetch'
import VectorBucketApi from './VectorBucketApi'
import {
  DeleteVectorsOptions,
  GetVectorsOptions,
  ListIndexesOptions,
  ListVectorsOptions,
  PutVectorsOptions,
  QueryVectorsOptions,
} from './types'

/**
 * Configuration options for the Storage Vectors client
 */
export interface StorageVectorsClientOptions {
  /**
   * Custom headers to include in all requests
   */
  headers?: { [key: string]: string }
  /**
   * Custom fetch implementation (optional)
   * Useful for testing or custom request handling
   */
  fetch?: Fetch
}

/**
 * Main client for interacting with S3 Vectors API
 * Provides access to bucket, index, and vector data operations
 *
 * **Usage Patterns:**
 *
 * 1. **Via StorageClient (recommended for most use cases):**
 * ```typescript
 * import { StorageClient } from '@supabase/storage-js'
 *
 * const storageClient = new StorageClient(url, headers)
 * const vectors = storageClient.vectors
 *
 * // Use vector operations
 * await vectors.createBucket('embeddings-prod')
 * const bucket = vectors.from('embeddings-prod')
 * await bucket.createIndex({ ... })
 * ```
 *
 * 2. **Standalone (for vector-only applications):**
 * ```typescript
 * import { StorageVectorsClient } from '@supabase/storage-js'
 *
 * const vectorsClient = new StorageVectorsClient('https://api.example.com', {
 *   headers: { 'Authorization': 'Bearer token' }
 * })
 *
 * // Access bucket operations
 * await vectorsClient.createBucket('embeddings-prod')
 *
 * // Access index operations via buckets
 * const bucket = vectorsClient.from('embeddings-prod')
 * await bucket.createIndex({
 *   indexName: 'documents',
 *   dataType: 'float32',
 *   dimension: 1536,
 *   distanceMetric: 'cosine'
 * })
 *
 * // Access vector operations via index
 * const index = bucket.index('documents')
 * await index.putVectors({
 *   vectors: [
 *     { key: 'doc-1', data: { float32: [...] }, metadata: { title: 'Intro' } }
 *   ]
 * })
 *
 * // Query similar vectors
 * const { data } = await index.queryVectors({
 *   queryVector: { float32: [...] },
 *   topK: 5,
 *   returnDistance: true
 * })
 * ```
 */
export class StorageVectorsClient extends VectorBucketApi {
  constructor(url: string, options: StorageVectorsClientOptions = {}) {
    super(url, options.headers || {}, options.fetch)
  }

  /**
   * Access operations for a specific vector bucket
   * Returns a scoped client for index and vector operations within the bucket
   *
   * @param vectorBucketName - Name of the vector bucket
   * @returns Bucket-scoped client with index and vector operations
   *
   * @example
   * ```typescript
   * const bucket = client.bucket('embeddings-prod')
   *
   * // Create an index in this bucket
   * await bucket.createIndex({
   *   indexName: 'documents-openai',
   *   dataType: 'float32',
   *   dimension: 1536,
   *   distanceMetric: 'cosine'
   * })
   *
   * // List indexes in this bucket
   * const { data } = await bucket.listIndexes()
   * ```
   */
  from(vectorBucketName: string): VectorBucketScope {
    return new VectorBucketScope(this.url, this.headers, vectorBucketName, this.fetch)
  }
}

/**
 * Scoped client for operations within a specific vector bucket
 * Provides index management and access to vector operations
 */
export class VectorBucketScope extends VectorIndexApi {
  private vectorBucketName: string

  constructor(
    url: string,
    headers: { [key: string]: string },
    vectorBucketName: string,
    fetch?: Fetch
  ) {
    super(url, headers, fetch)
    this.vectorBucketName = vectorBucketName
  }

  /**
   * Creates a new vector index in this bucket
   * Convenience method that automatically includes the bucket name
   *
   * @param options - Index configuration (vectorBucketName is automatically set)
   * @returns Promise with empty response on success or error
   *
   * @example
   * ```typescript
   * const bucket = client.bucket('embeddings-prod')
   * await bucket.createIndex({
   *   indexName: 'documents-openai',
   *   dataType: 'float32',
   *   dimension: 1536,
   *   distanceMetric: 'cosine',
   *   metadataConfiguration: {
   *     nonFilterableMetadataKeys: ['raw_text']
   *   }
   * })
   * ```
   */
  override async createIndex(options: Omit<CreateIndexOptions, 'vectorBucketName'>) {
    return super.createIndex({
      ...options,
      vectorBucketName: this.vectorBucketName,
    })
  }

  /**
   * Lists indexes in this bucket
   * Convenience method that automatically includes the bucket name
   *
   * @param options - Listing options (vectorBucketName is automatically set)
   * @returns Promise with list of indexes or error
   *
   * @example
   * ```typescript
   * const bucket = client.bucket('embeddings-prod')
   * const { data } = await bucket.listIndexes({ prefix: 'documents-' })
   * ```
   */
  override async listIndexes(options: Omit<ListIndexesOptions, 'vectorBucketName'> = {}) {
    return super.listIndexes({
      ...options,
      vectorBucketName: this.vectorBucketName,
    })
  }

  /**
   * Retrieves metadata for a specific index in this bucket
   * Convenience method that automatically includes the bucket name
   *
   * @param indexName - Name of the index to retrieve
   * @returns Promise with index metadata or error
   *
   * @example
   * ```typescript
   * const bucket = client.bucket('embeddings-prod')
   * const { data } = await bucket.getIndex('documents-openai')
   * console.log('Dimension:', data?.index.dimension)
   * ```
   */
  override async getIndex(indexName: string) {
    return super.getIndex(this.vectorBucketName, indexName)
  }

  /**
   * Deletes an index from this bucket
   * Convenience method that automatically includes the bucket name
   *
   * @param indexName - Name of the index to delete
   * @returns Promise with empty response on success or error
   *
   * @example
   * ```typescript
   * const bucket = client.bucket('embeddings-prod')
   * await bucket.deleteIndex('old-index')
   * ```
   */
  override async deleteIndex(indexName: string) {
    return super.deleteIndex(this.vectorBucketName, indexName)
  }

  /**
   * Access operations for a specific index within this bucket
   * Returns a scoped client for vector data operations
   *
   * @param indexName - Name of the index
   * @returns Index-scoped client with vector data operations
   *
   * @example
   * ```typescript
   * const index = client.bucket('embeddings-prod').index('documents-openai')
   *
   * // Insert vectors
   * await index.putVectors({
   *   vectors: [
   *     { key: 'doc-1', data: { float32: [...] }, metadata: { title: 'Intro' } }
   *   ]
   * })
   *
   * // Query similar vectors
   * const { data } = await index.queryVectors({
   *   queryVector: { float32: [...] },
   *   topK: 5
   * })
   * ```
   */
  index(indexName: string): VectorIndexScope {
    return new VectorIndexScope(
      this.url,
      this.headers,
      this.vectorBucketName,
      indexName,
      this.fetch
    )
  }
}

/**
 * Scoped client for operations within a specific vector index
 * Provides vector data operations (put, get, list, query, delete)
 */
export class VectorIndexScope extends VectorDataApi {
  private vectorBucketName: string
  private indexName: string

  constructor(
    url: string,
    headers: { [key: string]: string },
    vectorBucketName: string,
    indexName: string,
    fetch?: Fetch
  ) {
    super(url, headers, fetch)
    this.vectorBucketName = vectorBucketName
    this.indexName = indexName
  }

  /**
   * Inserts or updates vectors in this index
   * Convenience method that automatically includes bucket and index names
   *
   * @param options - Vector insertion options (bucket and index names automatically set)
   * @returns Promise with empty response on success or error
   *
   * @example
   * ```typescript
   * const index = client.bucket('embeddings-prod').index('documents-openai')
   * await index.putVectors({
   *   vectors: [
   *     {
   *       key: 'doc-1',
   *       data: { float32: [0.1, 0.2, ...] },
   *       metadata: { title: 'Introduction', page: 1 }
   *     }
   *   ]
   * })
   * ```
   */
  override async putVectors(options: Omit<PutVectorsOptions, 'vectorBucketName' | 'indexName'>) {
    return super.putVectors({
      ...options,
      vectorBucketName: this.vectorBucketName,
      indexName: this.indexName,
    })
  }

  /**
   * Retrieves vectors by keys from this index
   * Convenience method that automatically includes bucket and index names
   *
   * @param options - Vector retrieval options (bucket and index names automatically set)
   * @returns Promise with array of vectors or error
   *
   * @example
   * ```typescript
   * const index = client.bucket('embeddings-prod').index('documents-openai')
   * const { data } = await index.getVectors({
   *   keys: ['doc-1', 'doc-2'],
   *   returnMetadata: true
   * })
   * ```
   */
  override async getVectors(options: Omit<GetVectorsOptions, 'vectorBucketName' | 'indexName'>) {
    return super.getVectors({
      ...options,
      vectorBucketName: this.vectorBucketName,
      indexName: this.indexName,
    })
  }

  /**
   * Lists vectors in this index with pagination
   * Convenience method that automatically includes bucket and index names
   *
   * @param options - Listing options (bucket and index names automatically set)
   * @returns Promise with array of vectors and pagination token
   *
   * @example
   * ```typescript
   * const index = client.bucket('embeddings-prod').index('documents-openai')
   * const { data } = await index.listVectors({
   *   maxResults: 500,
   *   returnMetadata: true
   * })
   * ```
   */
  override async listVectors(
    options: Omit<ListVectorsOptions, 'vectorBucketName' | 'indexName'> = {}
  ) {
    return super.listVectors({
      ...options,
      vectorBucketName: this.vectorBucketName,
      indexName: this.indexName,
    })
  }

  /**
   * Queries for similar vectors in this index
   * Convenience method that automatically includes bucket and index names
   *
   * @param options - Query options (bucket and index names automatically set)
   * @returns Promise with array of similar vectors ordered by distance
   *
   * @example
   * ```typescript
   * const index = client.bucket('embeddings-prod').index('documents-openai')
   * const { data } = await index.queryVectors({
   *   queryVector: { float32: [0.1, 0.2, ...] },
   *   topK: 5,
   *   filter: { category: 'technical' },
   *   returnDistance: true,
   *   returnMetadata: true
   * })
   * ```
   */
  override async queryVectors(
    options: Omit<QueryVectorsOptions, 'vectorBucketName' | 'indexName'>
  ) {
    return super.queryVectors({
      ...options,
      vectorBucketName: this.vectorBucketName,
      indexName: this.indexName,
    })
  }

  /**
   * Deletes vectors by keys from this index
   * Convenience method that automatically includes bucket and index names
   *
   * @param options - Deletion options (bucket and index names automatically set)
   * @returns Promise with empty response on success or error
   *
   * @example
   * ```typescript
   * const index = client.bucket('embeddings-prod').index('documents-openai')
   * await index.deleteVectors({
   *   keys: ['doc-1', 'doc-2', 'doc-3']
   * })
   * ```
   */
  override async deleteVectors(
    options: Omit<DeleteVectorsOptions, 'vectorBucketName' | 'indexName'>
  ) {
    return super.deleteVectors({
      ...options,
      vectorBucketName: this.vectorBucketName,
      indexName: this.indexName,
    })
  }
}